Distributed MIMO for 6G sub-Networks in the Unlicensed Spectrum

In this paper, we consider the sixth generation (6G) sub-networks, where hyper reliable low latency communications (HRLLC) requirements are expected to be met. We focus on a scenario where multiple sub-networks are active in the service area and assess the feasibility of using the 6 GHz unlicensed spectrum to operate such deployment, evaluating the impact of listen before talk (LBT). Then, we explore the benefits of using distributed multiple input multiple output (MIMO), where the available antennas in every sub-network are distributed over a number of access points (APs). Specifically, we compare different configurations of distributed MIMO with respect to centralized MIMO, where a single AP with all antennas is located at the center of every sub-network.Comment: This paper is accepted for publication in 2023 IEEE Conference on Standards for Communications and Networking (CSCN

Integrating Pre-Earthquake Signatures From Different Precursor Tools

Potential earthquake precursors include, among others, electromagnetic fields, gas emissions, Land Surface Temperature (LST), Sea Surface Temperature (SST), and Surface Air Temperature (SAT) anomalies. These observables have been individually studied, before earthquakes, by many researchers. The ionospheric studies concerning earthquakes (EQs) using magnetic data from Low Earth Orbit (LEO) satellites are increasingly being used to detect ionospheric anomalies before large EQs. Also, LST, SST, and SAT values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) are considered as physical precursors before EQs. In this work, we jointly analyze magnetic, MODIS, and MERRA-2 data in space and time around the epicenters before the selected EQs in Mexico, Japan, Chile, and Indonesia. Our analyses present interesting findings where anomalies in temperature and magnetic field, preceding the considered EQs, are confirmed through different methods. Particularly, we utilize the Fast Fourier Transform (FFT) and the Discrete Cosine Transform (DCT) for analyzing magnetic data over the designated EQs regions. We use the magnetic data acquired by Swarm satellites in the top side ionosphere along with MODIS and MERRA-2. Five case studies are described to prove the effectiveness of our analyses. Precursory anomalies were observed using these methods in different anomalous days from the considered four regions of interest around the epicenter. It is concluded that these methods could be effective and reliable in detecting anomalies preceding the upcoming EQs

Power Control in Cell-Free Massive MIMO Networks for UAVs URLLC Under the Finite Blocklength Regime

In this paper, we employ a user-centric (UC) cell-free massive MIMO (CFmMIMO) network for providing ultra reliable low latency communication (URLLC) when traditional ground users (GUs) coexist with unmanned aerial vehicles (UAVs). We study power control in both the downlink and the uplink when partial zero-forcing (PZF) transmit/receive beamforming and maximum ratio transmission/combining are utilized. We consider optimization problems where the objective is to maximize either the users' sum URLLC rate or the minimum user's URLLC rate. The URLLC rate function is both complicated and nonconvex rendering the considered optimization problems nonconvex. Thus, we propose two approximations for the complicated URLLC rate function and employ successive convex optimization (SCO) to tackle the considered optimization problems. Specifically, we propose the SCO with iterative concave lower bound approximation (SCO-ICBA) and the SCO with iterative interference approximation (SCO-IIA). We provide extensive simulations to evaluate SCO-ICBA and SCO-IIA and compare UC CFmMIMO deployment with traditional colocated massive MIMO (COmMIMO) systems. The obtained results reveal that employing the SCO-IIA scheme to optimize the minimum user's rate for CFmMIMO with MRT in the downlink, and PZF reception in the uplink can provide the best URLLC rate performances.Comment: This paper has been accepted for publication on the IEEE Transactions on Communications. Only personal use of this material is permitted. Permission from IEEE must be obtained for all other use

Deep Learning for Joint Adaptations of Transmission Rate and Payload Length in Vehicular Networks

Recently, vehicular networks have emerged to facilitate intelligent transportation systems (ITS). They enable vehicles to communicate with each other in order to provide various services such as traffic safety, autonomous driving, and entertainments. The vehicle-to-vehicle (V2V) communication channel is doubly selective, where the channel changes within the transmission bandwidth and the frame duration. This necessitates robust algorithms to provide reliable V2V communications. In this paper, we propose a scheme that provides joint adaptive modulation, coding and payload length selection (AMCPLS) for V2V communications. Our AMCPLS scheme selects both the modulation and coding scheme (MCS) and the payload length of transmission frames for V2V communication links, according to the V2V channel condition. Our aim is to achieve both reliability and spectrum efficiency. Our proposed AMCPLS scheme improves the V2V effective throughput performance while satisfying a predefined frame error rate (FER). Furthermore, we present a deep learning approach that exploits deep convolutional neural networks (DCNN) for implementing the proposed AMCPLS. Simulation results reveal that the proposed DCNN-based AMCPLS approach outperforms other competing machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM) in terms of FER, effective throughput, and prediction time

A Deep Learning Model for Earthquake parameters Observation in IoT System-based Earthquake Early Warning

International audienc

Promoting fractional frequency reuse performance for combating pilot contamination in massive multiple input multiple output

Massive multiple-input multiple-output (MIMO) improves spectrum efficiency by increasing the capacity of the wireless structure. Therefore, massive MIMO is promising for fifth generation (5G) wireless communications. In massive MIMO, channel estimation is a crucial part that should achieve reliable performance. Pilots are sent from the end-users to be used for estimating the channel. However, the problem of interference in pilot contamination affects the performance for cell-edge users. Specifically, pilot contamination appears when the same pilot sequence is utilized at the same time by more than one terminal. This lead to an inaccurate estimation of the channel. Consequently, the decoded data will not be reliable. For mitigating these pilot contamination effects, an enhanced fractional frequency reuse (eFFR) scheme is proposed that uses an algorithm in the allocation of pilot sequences to end users’ devices based on the locations of the users from the target base station (BS). The simulation results exhibit that the proposed scenario outweighs the traditional FFR within both signal to interference, and noise ratio (SINR), and capacity. Consequently, the suggested scenario enhances the performance of more than 80% of the cell terminals and the other 20% of the terminals have a slightly lower performance compared to the FFR

Simultaneous Super-Resolution and Classification of Lung Disease Scans

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support